Last 7 Days (August 25 – August 31, 2026)
Search-augmented reasoning remains difficult for small language models. On-policy distillation (OPD) from trained teachers offers a promising direction, but suffers from two issues: (1) high-quality multi-turn search trajectories depend on dynamic retriever responses, making SFT data prohibitively expensive to collect at scale; (2) task-specifically trained teachers incur substantial training cost, while directly applying OPD with an off-the-shelf teacher without task-specific fine-tuning constrains the student to the teacher's performance ceiling and suffers from severe training instability. We propose OPDSearch+, the first distillation paradigm that requires no teacher fine-tuning for search-augmented reasoning. We investigate the role of a frozen off-the-shelf instruct model as the teacher in on-policy distillation, and reveal a key insight: the teacher reshapes the student's policy distribution so that subsequent RL converges to a superior solution that RL alone cannot reach. In stage one, the student interacts with a live search engine and is distilled via a per-position forward KL objective, transferring reasoning decomposition and evidence integration skills without any task-specific teacher training. In stage two, RL refines the distilled student from a richer behavioral foundation, achieving performance that RL alone cannot reach from scratch. Across seven QA benchmarks, OPDSearch+ with a 3B model consistently outperforms all prior 3B RL baselines, achieving gains of 13.1% on HotpotQA and 8.5% on 2WikiMultihopQA.
Primary: University of Chinese Academy of Sciences
All Institutions: University of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, University of Macau, Wuhan University, Georgia Institute of Technology, Northwestern Polytechnical University, University of Hong Kong
[One sentence main contribution]. OPDSearch+ introduces a novel two-stage on-policy distillation and RL refinement framework that leverages a frozen off-the-shelf teacher to stabilize training and enhance performance in search-augmented reasoning for small language models. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper presents a significant advancement in the field of search-augmented reasoning for small language models. By addressing the critical issues of data collection costs and training instability associated with traditional on-policy distillation, OPDSearch+ offers a practical and effective solution. The theoretical analysis of forward-KL divergence in this context is a valuable contribution, providing insights into gradient variance and entropy collapse. The empirical results demonstrate clear improvements over state-of-the-art baselines, particularly in multi-hop reasoning tasks. The work is well-motivated, rigorously evaluated, and has the potential to influence future research in agent-based learning and model distillation.
The paper proposes OPDSearch+, a two-stage framework for search-augmented reasoning in small language models (SLMs). Stage 1 uses On-Policy Distillation (OPD) with a frozen off-the-shelf teacher (Qwen2.5-14B-Instruct) to distill token-level distributions onto student-generated trajectories using a forward-KL objective. Stage 2 refines this distilled policy using Reinforcement Learning (RL) with Group Relative Policy Optimization (GRPO). The core theoretical contribution is the analysis of forward-KL vs. reverse-KL in the context of on-policy distillation for interactive agents, arguing that forward-KL provides better gradient variance control and prevents entropy collapse. The methodology is sound, addressing the distribution shift problem inherent in offline SFT for dynamic retrieval environments. The use of a frozen generalist teacher to provide implicit supervision for retrieval quality is a clever and cost-effective design choice.
The experimental evaluation is comprehensive, covering seven QA benchmarks (NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultihopQA, MuSiQue, Bamboogle). The results show that OPDSearch+ (3B) outperforms all prior 3B RL baselines, including Search-R1 and GiGPO, with significant gains on multi-hop tasks (e.g., +13.1% on HotpotQA). The ablation studies are thorough, comparing against offline SFT, pure RL, pure OPD, and joint optimization, confirming the superiority of the two-stage approach. The analysis of training dynamics and gradient stability supports the theoretical claims. The comparison with reverse-KL variants demonstrates the practical necessity of the proposed forward-KL objective.
The paper provides detailed hyperparameters, model versions (Qwen2.5-3B/14B), and training configurations (veRL framework, 4 H200 GPUs). The description of the loss functions and masking strategies is clear. The code is not explicitly linked in the text provided, but the method relies on standard components (GRPO, KL divergence) and open-source models, making reproduction feasible for researchers with similar resources. The specific clipping parameters and learning rates are mentioned, aiding reproducibility.
The primary limitation is the reliance on a significantly larger teacher model (14B) for distillation, which incurs inference costs during the training phase, although no fine-tuning of the teacher is required. The method is evaluated only on QA benchmarks; its performance on other reasoning tasks (e.g., mathematical reasoning, code generation) is not explored. The "frozen" teacher assumption might limit the transfer of highly task-specific nuances that a fine-tuned teacher could provide, although the authors argue this is mitigated by the subsequent RL stage. The computational cost of generating on-policy trajectories with a live search engine is non-trivial.
This work contributes to the democratization of advanced reasoning capabilities by enabling small models to perform complex search-augmented tasks efficiently. It reduces the dependency on expensive, task-specific teacher training, lowering the barrier to entry for developing capable search agents. The insights into KL divergence directions for stability in interactive settings are broadly applicable to other agent-based learning scenarios. [One sentence main contribution]. OPDSearch+ introduces a novel two-stage on-policy distillation and RL refinement framework that leverages a frozen off-the-shelf teacher to stabilize training and enhance performance in search-augmented reasoning for small language models. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper presents a significant advancement in the field of search-augmented reasoning for small language models. By addressing the critical issues of data collection costs and training instability associated with traditional on-policy distillation, OPDSearch+ offers a practical and effective solution. The theoretical analysis of forward-KL divergence in this context is a valuable contribution, providing insights into gradient variance and entropy collapse. The empirical results demonstrate clear improvements over state-of-the-art baselines, particularly in multi-hop reasoning tasks. The work is well-motivated, rigorously evaluated, and has the potential to influence future research in agent-based learning and model distillation.
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simply by specifying it in the context, without any parameter update. This form of in-context learning (ICL) turns generalization into a problem of task specification. To achieve cross-task generalization, we bring this paradigm to robotic manipulation, and argue that the natural task specification for manipulation is a human video: unlike language, it provides rich visual cues about the intended task evolution. We present Zero-WAM, a causal video-action model that executes unseen tasks by following in-context human video guidance. To address the scarcity of task-rich paired human-robot data, we propose an automatic pipeline that converts task-sampled robot trajectories into semantically matched human videos, yielding HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks. For model training, we further introduce an in-context future chunk prediction (IFP) objective that suppresses shortcuts learned from seen tasks and forces the policy to draw task information from the video prompt. On seven unseen tasks in RoboTwin 2.0 simulation, Zero-WAM achieves a 47.0% average success rate, an absolute improvement of 29.5 percentage points over the strongest video-action baseline. In real-world evaluations, it follows human video guidance to generalize to unseen task configurations involving multi-object scenes, long-horizon manipulation, and fine-grained insertion.
Primary: Ant Group (Robby Ant Research)
All Institutions: Ant Group (Robby Ant Research)
[One sentence main contribution]. [Zero-WAM introduces a novel in-context learning framework for robotic manipulation that leverages automatically generated human video demonstrations and a specialized future chunk prediction objective to achieve significant zero-shot cross-task generalization performance.]
The paper proposes Zero-WAM, a causal video-action model designed for zero-shot cross-task generalization in robotic manipulation. The core innovation lies in treating human video demonstrations as in-context learning (ICL) prompts, analogous to how LLMs use text instructions. To overcome the scarcity of paired human-robot data, the authors introduce an automatic pipeline (HumanGen) that synthesizes human videos from robot trajectories using VLMs and video generation models. A key technical contribution is the In-Context Future Chunk Prediction (IFP) objective, which prevents the model from relying on shortcuts from robot history by forcing it to predict strided future video chunks based on the current state, thereby encouraging the encoding of task semantics from the human video prompt. The architecture utilizes a Mixture-of-Transformers design, separating video and action prediction while sharing attention layers.
The evaluation is conducted on the RoboTwin 2.0 simulation environment and real-world Franka robot setups. The paper reports a significant improvement over strong baselines (LingBot-VA and WAN-Action), achieving a 47.0% average success rate on seven unseen tasks, an absolute improvement of 29.5 percentage points. The results are supported by ablation studies isolating the effects of ICL, IFP, and task-balanced data sampling. Real-world experiments further demonstrate generalization to unseen configurations in multi-object scenes and fine-grained insertion tasks. The statistical significance is addressed by reporting results over multiple seeds and trials.
The paper provides detailed implementation details, including model architecture (Wan-2.2 backbone), training hyperparameters, and data composition. The dataset (HumanGen) and code are likely to be released given the project URL, though the current text does not explicitly confirm code availability. The methodology for generating synthetic human videos is described in sufficient detail for replication.
The reliance on synthetic human videos introduces potential domain gaps or "hallucinations" in the generated demonstrations, although the paper claims semantic preservation. The current evaluation is limited to stationary tabletop manipulation; generalization to mobile manipulation or highly dynamic environments is not demonstrated. The success on the "stack blocks three" task remains low (9%), indicating challenges with long-horizon, complex spatial reasoning.
This work advances the field of robot learning by providing a scalable path to zero-shot generalization using abundant human video data. It reduces the dependency on expensive, task-specific robot demonstrations. The potential for widespread adoption of human-video-guided policies could accelerate the deployment of general-purpose robots in unstructured environments. [One sentence main contribution]. [Zero-WAM introduces a novel in-context learning framework for robotic manipulation that leverages automatically generated human video demonstrations and a specialized future chunk prediction objective to achieve significant zero-shot cross-task generalization performance.]
We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO). For OLO over the probability simplex $Δ_d$, we give an algorithm with $O(\log d)$ alternating regret that remains a constant for any time horizon $T$, and a matching lower bound. Our constant regret bound significantly improves previous results with $O(\log ^{2/3}d \cdot T^{1/3})$ regret [Cevher, Cutkosky, Kavis, Piliouras, Skoulakis, Viano, NeurIPS 2023, Hait, Li, Luo, Zhang, COLT 2025]. As a result, we obtain alternating learning dynamics with $O(\log d /T)$ convergence to Nash equilibria in two-player zero-sum games and $O(\log d /T)$ convergence to coarse correlated equilibria in two-player general-sum games. This is the first uncoupled learning dynamics with $O(1/T)$ convergence to CCE in two-player general-sum games, while all prior works suffer additional $\log T$ factors. For general OCO over a $d$-dimensional compact convex set, we give an algorithm with $O(d\log (1+T/d))$ alternating regret, improving the previous best of $\widetilde{O}(d^{2/3}T^{1/3})$. We also prove a matching lower bound of $Ω(d\log (1+T/d))$, showing that the $Ω(\log T)$ factor is unavoidable.
Primary: Shanghai University of Finance and Economics
All Institutions: Shanghai University of Finance and Economics, Yale University
The paper makes a significant theoretical contribution by establishing the minimax-optimal alternating regret for OLO and OCO, providing tight upper and lower bounds that improve upon previous state-of-the-art results and enabling faster convergence rates in uncoupled learning dynamics for games.
The paper proposes a novel online learning algorithm, "Alternation-Aware Hedge" (AA-Hedge), specifically designed to minimize alternating regret in Online Linear Optimization (OLO) and Online Convex Optimization (OCO). The core methodological innovation lies in exploiting the structure of alternating updates in two-player games, where the learner observes the opponent's previous move before making their own. The authors derive a potential function analysis that yields a constant $O(d)$ alternating regret for OLO over the simplex, a significant improvement over previous $O(T^{1/3})$ bounds. For general OCO, they propose a continuous variant ("Continuous AA-Hedge") achieving $O(d(1+T/d))$ regret. The theoretical framework is rigorous, involving detailed proofs of feasibility, potential monotonicity, and matching lower bounds for both settings. The extension from discrete simplex to general convex bodies via measure-theoretic arguments is technically sophisticated.
The paper is primarily theoretical. It does not include empirical experiments on standard game benchmarks or large-scale simulations. The "evaluation" consists of theoretical lower bound constructions and asymptotic complexity analysis. While the theoretical results are strong, the lack of empirical validation on practical game scenarios (e.g., poker variants, large-scale matrix games) limits the immediate assessment of practical performance compared to the theoretical bounds. However, in theoretical ML research, rigorous bounds often suffice for high-impact publication if the problem is well-posed and the bounds are tight.
The algorithms (AA-Hedge and Continuous AA-Hedge) are fully described with pseudocode and parameter settings. The proofs are provided in the appendix. The work is highly reproducible from a theoretical standpoint. The authors acknowledge the use of AI assistants for proof generation, which is a transparency note but does not invalidate the mathematical content provided it is verified (which the text claims was done).
The primary limitation is the lack of empirical validation. The theoretical bounds are asymptotic; finite-sample performance or behavior on non-convex/non-smooth variants of games is not addressed. Additionally, the "alternating" setting assumes a specific turn-based structure which, while common in some game-playing contexts (like CFR variants), is less standard than simultaneous move dynamics in many general ML applications. The $O(d)$ constant might be large in practice for high-dimensional $d$, though it is independent of $T$.
This work settles a long-standing open question regarding the minimax-optimal alternating regret. It provides a theoretical foundation for faster convergence in uncoupled learning dynamics for general-sum games, specifically achieving the first $O(1/T)$ convergence to Coarse Correlated Equilibria (CCE) without logarithmic factors. This has significant implications for the design of efficient, decentralized learning algorithms in multi-agent systems and game theory. The paper makes a significant theoretical contribution by establishing the minimax-optimal alternating regret for OLO and OCO, providing tight upper and lower bounds that improve upon previous state-of-the-art results and enabling faster convergence rates in uncoupled learning dynamics for games.
Factual question answering (QA) typically assumes a single canonical answer, obscuring whether large language models (LLMs) retain divergent accounts of long-tail facts. To address this gap, we introduce ElephantBench, a closed-book knowledge probe comprising 1,094 questions generated through an auditable graph-based pipeline. The pipeline retrieves related documents from a low-exposure web corpus, identifies naturally occurring disagreements, and converts them into multi-account QA records. Each answer is verified against the originating documents and authoritative public web sources and is then reviewed by human annotators. Across 32 models, even the strongest model recovers both accounts on only 52.4% of questions, while on nearly all remaining questions it recalls one account but omits the other. Scaling model size and inference-time reasoning improve recall but do not eliminate this incompleteness. Corpus analysis further shows that exposure imbalance favors the dominant account, whereas greater minority-side exposure is associated with more complete recall. These findings establish ElephantBench as a reproducible knowledge probe for diagnosing epistemic myopia in parametric memory. More broadly, our graph-based benchmark construction pipeline provides an efficient and scalable way to turn long-tail corpora into source-traceable knowledge probes, supporting efforts to evaluate and advance the epistemic rigour of next-generation LLMs. Code is available at https://github.com/Tencent/ElephantBench.
Primary: Tencent
All Institutions: Tencent
The paper introduces ElephantBench, a closed-book benchmark derived from low-exposure web corpora to probe the "epistemic myopia" of LLMs, revealing that even frontier models fail to recall all verified accounts of long-tail facts in nearly half of cases. By utilizing a graph-based pipeline to mine natural source disagreements and verifying them through multi-stage auditing, the study provides a rigorous diagnostic tool that distinguishes between factual retention and memory completeness, offering critical insights into the limitations of parametric knowledge and the impact of data exposure imbalance on model behavior.
The paper introduces a novel benchmark construction pipeline that inverts standard data selection by mining the "low-exposure" remainder of web corpora (documents filtered out by quality classifiers like DCLM) to identify naturally occurring factual disagreements. By constructing a document graph where edges represent support or conflict between accounts of the same fact, the authors create a closed-book probe for "epistemic myopia." The methodology is rigorous, involving LLM-based edge classification, subgraph sampling for QA generation, and a multi-stage verification process (LLM check, web agent verification, and human review). The use of a graph-based approach to isolate conflicting facts is a clever and scalable solution to the problem of finding multi-account facts at scale.
The evaluation is extensive, covering 32 models (26 open-weight, 6 proprietary). The key finding is that even the strongest models achieve complete recall of all verified accounts for only ~52% of questions, revealing a significant gap between factual retention and completeness. The analysis of exposure imbalance is particularly strong, showing that minority-side exposure is more predictive of complete recall than majority-side exposure. The ablation on reasoning modes reveals a counter-intuitive result where reasoning can sometimes degrade completeness in smaller models by suppressing less salient accounts. The inclusion of a perplexity-based proxy for evaluation adds practical value for efficient benchmarking.
The paper provides high reproducibility. The code and dataset are released under Apache 2.0. The construction pipeline is detailed with specific prompts and hyperparameters. The use of standard tools (DCLM, T-NER, SuperGPQA taxonomy) and open-source models for construction ensures that the benchmark can be regenerated or extended. The evaluation protocol is clearly defined, including the judge prompt and scoring rubric.
The primary limitation is the reliance on a specific low-exposure corpus (RePro organic data) as a proxy for general web exposure, which may not perfectly align with the training data of all evaluated models. The benchmark size (1,094 questions) is moderate, and the 22 knowledge fields, while diverse, may not cover the full breadth of long-tail knowledge. The observational nature of the exposure analysis means causal claims cannot be made.
This paper has significant implications for the evaluation of LLMs, shifting the focus from single-answer accuracy to the completeness of parametric memory. It highlights a critical failure mode (epistemic myopia) that is obscured by traditional QA benchmarks. The findings on exposure imbalance provide actionable insights for data curation strategies, suggesting that balancing exposure to minority accounts is crucial for improving model completeness. The benchmark serves as a valuable diagnostic tool for the field. The paper introduces ElephantBench, a closed-book benchmark derived from low-exposure web corpora to probe the "epistemic myopia" of LLMs, revealing that even frontier models fail to recall all verified accounts of long-tail facts in nearly half of cases. By utilizing a graph-based pipeline to mine natural source disagreements and verifying them through multi-stage auditing, the study provides a rigorous diagnostic tool that distinguishes between factual retention and memory completeness, offering critical insights into the limitations of parametric knowledge and the impact of data exposure imbalance on model behavior.
An LLM agent shown a professional-looking market panel commits to a directional call on a provably unpredictable question far more often than one asked the bare question: across 12 frontier models, commitment rises from 6.5% to 54.0% as evidence is escalated. It commits just as readily when every number on the panel is invented: fabricating the entire display, so nothing the model can see is true except the question itself, still lifts commitment from 24.5% to 36.8%, statistically indistinguishable from the 37.6% produced by genuine market data. What unlocks confident action is not information but the authority of its packaging. The failure is narrow and locatable. Incapacity is not the answer: on matched answerable questions attached to the same panels, the same models answer essentially always, at near-perfect accuracy. Nor is it belief - stated probabilities barely move across the gradient that swings action by 48 points, and score worse than a climatological baseline. Missing judgment isn't it either: asked to classify a question's knowability before acting, models call it irreducible 90% of the time and then commit on just 0.4% of those. The act/don't-act gate is what fails, and the effect is concentrated in a few models rather than universal. Because the gate is separable, it can be trained. Supervised fine-tuning of a 3B model on 540 synthetic cases, predominantly dice, coins, jars and timers, drives commitment to 0.0% on the original cases and transfers to three unseen domains. It does not survive everything: the gate holds exactly when the response format leaves room to reason, and rigid formats that remove that room leave the model confident and wrong on questions it otherwise answers correctly. The gate is trainable and context-fragile, and deployment needs both halves of that sentence.
Primary: Independent Researcher
All Institutions: Independent Researcher
This paper has profound broader implications for the development, deployment, and governance of LLM agents. It highlights a critical vulnerability: the "authority of packaging" can override an LLM's inherent judgment about knowability, leading to confident but unearned commitments on unpredictable questions. This directly impacts AI safety and reliability, as agents in real-world settings (e.g., finance, healthcare) are often presented with authoritative-looking data. The finding that action calibration is distinct from belief calibration necessitates a shift in evaluation paradigms, moving beyond stated probabilities to auditing decisions. The proposed "commitment rate on aleatoric probes" as a cheap, reproducible robustness test offers a concrete measurement artifact for compliance with emerging regulations like the EU AI Act (Article 15) and NIST AI RMF. The demonstration of a trainable gate provides a pathway for mitigation, but its context-fragility underscores the need for careful prompt engineering and robust deployment strategies, emphasizing that "deployment needs both halves of that sentence." This work is a significant step towards building more trustworthy and appropriately cautious AI agents. This paper makes a crucial contribution by demonstrating that LLM agents are susceptible to making confident, unearned commitments on unknowable questions when presented with authoritative-looking, even fabricated, evidence. The rigorous methodology causally isolates the effect of presentation over information, localizes the failure to an act/don't-act gate, and shows this gate can be trained with synthetic data, though it remains context-fragile. This work provides significant empirical findings that challenge current assumptions about LLM calibration and reliability, offering concrete implications for agent evaluation, safety, and responsible deployment.
The methodology is exceptionally rigorous and well-designed. The core innovation lies in the "unknowability oracle" for aleatoric questions (short-horizon price direction, sports outcomes, weather) with sealed outcomes, ensuring provable unpredictability. The evidence gradient, escalating from bare questions to rich, professional-looking panels, is systematically applied. Crucially, the paper introduces a "fabricated evidence" condition where entire data panels are invented while preserving their authoritative form and internal consistency, allowing for causal isolation of presentation from information. The agentic protocol, offering ANSWER, CALL_TOOL, or DECLINE options, directly measures action rather than just stated probabilities. The use of matched answerable controls is vital for distinguishing a true knowability gate from a blanket decliner. The paper employs a comprehensive suite of metrics, including commitment rate, Youden's J for discrimination, Brier score for earned check, and CORP decomposition for calibration, all carefully defined and justified against degenerate strategy baselines. The supervised fine-tuning (SFT) approach on a 3B model using synthetic data from unrelated domains (dice, coins, timers) is a clever strategy to teach the abstention behavior, and the subsequent testing for transfer and fragility is thorough. The transparency in discussing parser choices (strict vs. semantic) and their impact is commendable.
The experimental evaluation is extensive, spanning 12 frontier models, 11 trained checkpoints, and four distinct domains (equity, crypto, sports, weather). The headline finding—commitment rising from 6.5% to 54.0% with escalating evidence, and fabricated evidence inducing commitment statistically indistinguishable from genuine data—is robust and highly impactful. The disaggregated results reveal that the effect is concentrated in specific models, not universal, which is an important nuance. The "dial, not a switch" observation, showing commitment scales with the quantity of authoritative display, further strengthens the causal link. The localization experiments effectively rule out incapacity, absent belief (stated probabilities barely move and are anti-predictive), and absent judgment (models classify questions as irreducible 90% of the time before acting). The trainability of the gate is demonstrated convincingly, with SFT driving commitment to 0.0% on original cases and transferring to unseen domains, surviving a tense-balanced control that rules out a simple grammatical heuristic. However, the critical finding in Section 8, that the trained gate's robustness is context-fragile and breaks when the response format removes room for reasoning, is a crucial insight for deployment. The detailed reporting of run-to-run variability and the impact of training data composition (e.g., removing sports items) further enhances the credibility of the results.
The paper demonstrates a strong commitment to reproducibility. It explicitly links to a GitHub repository containing code, data, pre-registration, and all cached model outputs. The training recipe (SFT with QLoRA on Qwen2.5-3B-Instruct, 3 epochs, 540 synthetic cases) is detailed. Prompt examples for both training and evaluation are provided verbatim in Appendix B. The two parsing strategies (strict and semantic) are clearly defined, and their delta is reported, ensuring transparency in how model outputs were interpreted. The paper also discusses potential sources of variability, such as greedy decoding not being bit-reproducible and uncontrolled serving configurations for hosted models, which adds to its credibility. The pre-registration details in Appendix A, distinguishing between decisions made before and after data existence, are exemplary for scientific rigor.
The authors are highly transparent about the limitations. Six open threats are acknowledged: (1) Weather is the weakest instrument due to genuine skill in its ensemble rain probability and lack of sealed outcomes. (2) The finding that only a subset of frontier models are seduced, meaning pooled rates aren't universal. (3) A residual lexical cue in synthetic unknowable items (e.g., "fair," "hidden") not fully excluded, though transfer domains speak against it. (4) The intervention being limited to one 3B model at one scale, meaning generalizability to larger models is not established. (5) Lower bounds for discrimination scores for two models due to high tool calls. (6) Cell-level values being approximate due to greedy decoding variability and uncontrolled serving configurations. My assessment aligns with these, and the paper's proactive disclosure of these limitations strengthens its overall scientific integrity.
This paper has profound broader implications for the development, deployment, and governance of LLM agents. It highlights a critical vulnerability: the "authority of packaging" can override an LLM's inherent judgment about knowability, leading to confident but unearned commitments on unpredictable questions. This directly impacts AI safety and reliability, as agents in real-world settings (e.g., finance, healthcare) are often presented with authoritative-looking data. The finding that action calibration is distinct from belief calibration necessitates a shift in evaluation paradigms, moving beyond stated probabilities to auditing decisions. The proposed "commitment rate on aleatoric probes" as a cheap, reproducible robustness test offers a concrete measurement artifact for compliance with emerging regulations like the EU AI Act (Article 15) and NIST AI RMF. The demonstration of a trainable gate provides a pathway for mitigation, but its context-fragility underscores the need for careful prompt engineering and robust deployment strategies, emphasizing that "deployment needs both halves of that sentence." This work is a significant step towards building more trustworthy and appropriately cautious AI agents. This paper makes a crucial contribution by demonstrating that LLM agents are susceptible to making confident, unearned commitments on unknowable questions when presented with authoritative-looking, even fabricated, evidence. The rigorous methodology causally isolates the effect of presentation over information, localizes the failure to an act/don't-act gate, and shows this gate can be trained with synthetic data, though it remains context-fragile. This work provides significant empirical findings that challenge current assumptions about LLM calibration and reliability, offering concrete implications for agent evaluation, safety, and responsible deployment.
Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be learned? For the former, elegant answers often take the form of combinatorial dimensions. The latter question, however, has proved considerably more elusive: all known general-purpose multiclass learners rely on intricate orientations of exponentially large one-inclusion structures, and familiar algorithmic principles such as proper learning and regularization remain poorly understood. Motivated by prior work, we ask whether learning reduces to proper learning---possibly over a larger hypothesis class---and whether proper or improper multiclass learning can ultimately be captured by suitable regularizers. Our primary results answer both questions negatively, resolving three open problems from prior work. First, we exhibit a learnable multiclass problem that cannot be embedded in any properly learnable class, meaning learning cannot be reduced to proper learning by enlarging the hypothesis class. Second, we demonstrate that proper learning can require training error and characterize this phenomenon precisely: every properly learnable class admits a proper learner making $o(m)$ errors on samples of size $m$, but every prescribed sublinear scale $a_m=o(m)$ is necessary for some properly learnable problem. Third, regularization is not a general learner: we exhibit a properly learnable class that cannot be learned by any Structural Risk Minimization (SRM) learner, and a learnable class that cannot be learned by any local regularizer. We complement these impossibility results with a positive theory that gives two sufficient conditions for SRM learnability and characterizes SRM representability through integrability of revealed preferences.
Primary: University of Washington
All Institutions: University of Washington, Northwestern University, University of Chicago
The paper resolves three major open problems in statistical learning theory by proving that learning cannot be reduced to proper learning and that regularization is not a general learner, providing a rigorous theoretical framework for understanding the limits of these fundamental algorithmic principles.
The paper employs rigorous theoretical methods from combinatorics and functional analysis to address fundamental questions in statistical learning theory. It constructs specific counterexamples (learnable but not properly learnable classes, classes not learnable by SRM) to prove negative results. The methodology is sophisticated, utilizing concepts like the DS dimension, one-inclusion structures, and integrability of revealed preferences to characterize the boundaries of algorithmic principles like proper learning and regularization. The use of AI (ChatGPT 5.6 Pro) in the discovery of constructions is noted, but the core contribution remains the mathematical proofs and theoretical frameworks.
As a purely theoretical paper, there are no empirical experiments, datasets, or benchmarks. The "evaluation" consists of the validity and tightness of the mathematical proofs and the resolution of open problems. The results are definitive within the theoretical framework.
The results are mathematically reproducible in the sense that the proofs can be verified. There is no code or experimental setup to reproduce. The transparency regarding the use of AI in the discovery process is a positive step for reproducibility of the research process, though it raises questions about the novelty of the specific constructions.
The paper is highly theoretical and may have limited immediate practical impact on algorithm design. The reliance on AI for key constructions, while disclosed, might lead to skepticism about the depth of human insight in the specific combinatorial designs, although the authors claim full responsibility. The results are negative (impossibility results), which, while valuable, do not provide new algorithms.
The paper significantly impacts the field of statistical learning theory by clarifying the limits of standard algorithmic principles. It guides future research by showing where proper learning and regularization fail, potentially steering researchers toward more nuanced or problem-specific approaches. It resolves long-standing open problems, contributing to the foundational understanding of multiclass learning. The paper resolves three major open problems in statistical learning theory by proving that learning cannot be reduced to proper learning and that regularization is not a general learner, providing a rigorous theoretical framework for understanding the limits of these fundamental algorithmic principles.
Powerful code agents can execute scripts, call tools, and manage files, yet many important applications remain accessible primarily through graphical user interfaces. We argue that screenshot-and-click is an inefficient interface for software-operating agents: screenshots are state-incomplete, and GUI actions are brittle, semantically weak, and poorly matched to long-horizon planning. We introduce ASIL (Agent-Software Interaction Layer), an agent-native interface that exposes software through structured JSON observations and code-executable semantic actions, realized through the deepest feasible access path for each application. We instantiate ASIL across 15 applications and a benchmark of 300 single-application and 80 multi-application tasks. ASIL reaches above 80 with closed models while executing fewer than five actions per task. Under a repaired runtime and a 50-step screenshot budget, the same tasks yield 6.6 and 26.6 strict success under screenshot-and-click control, rising to 15.0 and 53.3 on an easier OSWorld-comparable band. Against application-native interfaces on matched tasks, ASIL exceeds LibreOffice's UNO API by 28-38 strict points but only matches draw.io's MCP content contract. The structured modality also suits training: small-scale SFT raises Qwen3.5-2B from 58.0 to 72.1 and Qwen3.5-9B from 66.6 to 80.4, and resource-limited on-policy RL further raises them to 74.4 and 82.2.
Primary: Shanghai Jiao Tong University
All Institutions: Shanghai Jiao Tong University, State Key Laboratory for General Artificial Intelligence, X-LANCE Lab
[One sentence main contribution]. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper introduces ASIL, an agent-native interface that replaces screenshot-and-click with structured state and semantic actions, demonstrating significant improvements in task success and training efficiency for software-operating agents. The technical contribution is robust, with a well-defined interface contract, a comprehensive benchmark, and rigorous experiments showing that structured interfaces are superior to pixel-based ones for both inference and training. The significance to the field is high, as it challenges the prevailing paradigm of GUI agents and offers a practical, scalable solution for operating complex software through agents. The work is likely to influence future designs of agent interfaces and evaluation benchmarks.
The paper proposes ASIL (Agent-Software Interaction Layer), a framework that replaces the standard "screenshot-and-click" interface for GUI agents with structured JSON observations and semantic, code-executable actions. The core methodological contribution is the "ASILization pipeline," which maps heterogeneous applications (file-backed, script-based, API-based) to a unified agent-native interface. This approach is conceptually sound, drawing parallels to the success of code agents (SWE-agent, CodeAct) in software engineering. By exposing internal state (e.g., ODF XML, Blender Python scene graph, REST endpoints) rather than pixel data, the method reduces the action space from low-level motor primitives to high-level semantic operations. The methodology is rigorous in its definition of the interface contract (observe, execute, validate) and provides a semi-automatic onboarding process for new applications. However, the novelty is somewhat incremental as it essentially applies existing principles of API-based agents to GUI software, rather than introducing a fundamentally new architectural paradigm. The "deepest feasible access path" principle is a practical engineering solution rather than a theoretical breakthrough.
The experimental setup is extensive, featuring a 380-task benchmark across 15 applications, including single-app and multi-app tasks. The results are compelling: ASIL achieves >80% success with closed models using <5 actions, whereas screenshot-and-click baselines struggle significantly (6.6-26.6% strict success). The inclusion of training experiments (SFT and RL) on smaller models (Qwen3.5-2B/9B) demonstrates that the structured modality is more sample-efficient for training, yielding double-digit gains. The ablation studies on realization patterns (file vs. script vs. API) and the comparison against native interfaces (LibreOffice UNO, draw.io MCP) add depth. However, the comparison is somewhat biased because the ASIL prompts include evaluator-derived hints while GUI prompts do not, although the authors acknowledge this and provide hint-off comparisons in the camera-ready version. The "repaired GUI" baseline is a strong control, but the gap remains massive, suggesting the interface change is the primary driver of performance.
The paper provides detailed implementation details, including adapter contracts, observation schemas, and training hyperparameters. The release of task definitions, validators, adapter code, and training data enhances reproducibility. The use of standard open-source models (Qwen) and common frameworks (Verl, vLLM) further supports reproducibility. The semi-automatic onboarding pipeline is described in detail, allowing other researchers to extend ASIL to new applications. The benchmark is shared, enabling direct comparison of different agent interfaces.
The main limitation is the coverage gap for fully opaque, closed-source applications that lack open file formats, scripting interfaces, or service APIs. ASIL requires at least one "access door" to be open. Additionally, the method struggles with intrinsically perceptual tasks (e.g., aesthetic judgment in image editing) where structured state is insufficient. The prompt asymmetry in the original comparison, while addressed in the camera-ready version, remains a point of caution. The training gains for the 2B model on hard tasks are less consistent, indicating that small models may still struggle with long-horizon planning even with better interfaces.
This paper has significant potential impact on the field of AI agents. By demonstrating that structured interfaces outperform pixel-based ones for software operation, it provides a clear direction for future research in agent-computer interaction. The framework could be widely adopted to improve the reliability and efficiency of agents operating in real-world software environments. The training efficiency gains suggest that ASIL could enable the development of smaller, more capable agents for specific software domains. The work bridges the gap between code agents and GUI agents, offering a unified view of agent-software interaction. [One sentence main contribution]. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper introduces ASIL, an agent-native interface that replaces screenshot-and-click with structured state and semantic actions, demonstrating significant improvements in task success and training efficiency for software-operating agents. The technical contribution is robust, with a well-defined interface contract, a comprehensive benchmark, and rigorous experiments showing that structured interfaces are superior to pixel-based ones for both inference and training. The significance to the field is high, as it challenges the prevailing paradigm of GUI agents and offers a practical, scalable solution for operating complex software through agents. The work is likely to influence future designs of agent interfaces and evaluation benchmarks.
Agent skills bundle instructions, reference data, and executable helpers that let a general agent perform specialized tasks. Hosted providers can keep these files secret while selling access to task results, making the skill itself a valuable target. Existing disclosure defenses can block requests that ask for the skill or reproduce its text, but they cannot block customers from submitting the ordinary tasks the service is built to complete. We present Daydreaming, an execution-only attack that steals a multi-file skill through black-box task interactions. The victim is never asked to reveal the skill or grade a reconstruction. Instead, Daydreaming adaptively creates crafted tasks whose results distinguish possible hidden behaviors. It tests individual behaviors, uses attacker-controlled shadow agents to choose a design, and completes each file using stored victim results and local execution checks. We formalize three nested threat levels of access as Differential, Trace, and Output, and focus on Output, where the attacker sees only the final response and returned files. Across 7 skills and 4 victim models, Daydreaming recovers 86.8% of the original skill's capability at Output, outperforming SigLeak by almost 4x. It produces installable skills using a median of 32 victim calls per skill even with disclosure defenses enabled. These results show that hiding skill files and filtering direct disclosure do not, by themselves, prevent functional reconstruction through normal use.
Primary: UC Berkeley
All Institutions: UC Berkeley, National Yang Ming Chiao Tung University
Daydreaming presents a novel execution-only attack that reconstructs hidden agent skills through black-box task interaction, outperforming existing methods by 4x. The paper rigorously formalizes the threat landscape of Skill-as-a-Service, demonstrating that standard disclosure defenses are ineffective against adaptive behavioral inference, thereby establishing a new baseline for agentic security research.
The paper introduces "Daydreaming," a sophisticated black-box attack framework for stealing proprietary agent skills (instructions + code + data) from hosted services. The core methodological contribution is the formalization of three nested observability levels (Differential, Trace, Output) and the design of a hierarchical hypothesis refinement loop that operates effectively under the most restrictive "Output" setting. The approach cleverly uses "discriminating tasks"—inputs crafted specifically to distinguish between competing hypotheses about the hidden skill's logic—combined with local "shadow agents" to simulate and verify potential behaviors without direct access to the victim's internals. This shifts the paradigm from simple prompt extraction to behavioral system identification, which is a significant conceptual advance in AI security.
The evaluation is rigorous, testing across 7 diverse skills and 4 victim models. The paper demonstrates that Daydreaming recovers 86.8% of the original skill's capability using only final outputs, significantly outperforming prior baselines like SigLeak (by ~4x). The use of normalized success rates (NSR) and behavioral utility (NU) provides a fair comparison against the original skill. The ablation studies on query budgets and component contributions further validate the method's efficiency and robustness. The finding that structural recovery (exact file matching) is low while behavioral recovery is high is a critical insight for defenders.
The paper provides a clear algorithmic description (Algorithm 1) and detailed descriptions of the three stages. While the code repository is currently anonymous (typical for pre-publication), the detailed prompts and parameter settings in the appendices suggest high reproducibility. The use of standard benchmarks (SkillsBench) and open-weight models for some experiments aids in verification.
The attack relies on the attacker having a capable "attacker model" (Gemini-3.7-flash in experiments) to generate hypotheses and shadow agents; if the attacker's model is significantly weaker than the victim's, the attack's efficacy may drop. Additionally, the evaluation is limited to 7 skills, which, while diverse, may not cover all possible skill structures. The assumption that the attacker can execute returned code locally (for verification) is a strong privilege that may not always be available in strict SaaS environments.
This paper has high impact on the emerging field of Agentic AI security. It demonstrates that traditional "prompt injection" defenses are insufficient against sophisticated behavioral cloning attacks. It forces service providers to consider not just text leakage but functional leakage through standard task execution. This will likely lead to new defensive mechanisms focused on output obfuscation, rate limiting, or differential privacy in agent responses. Daydreaming presents a novel execution-only attack that reconstructs hidden agent skills through black-box task interaction, outperforming existing methods by 4x. The paper rigorously formalizes the threat landscape of Skill-as-a-Service, demonstrating that standard disclosure defenses are ineffective against adaptive behavioral inference, thereby establishing a new baseline for agentic security research.
Self-evolving LLM coding agents write their own tools by imitating retrieved skills from shared skill libraries. We identify a vulnerability in this loop: during authoring, a retrieved malicious skill can become the template for a new skill that preserves the payload. We call this self-poisoning: the agent authors, stores, and runs the resulting malicious skill. We exploit it through EvoMal, an attack that amplifies self-poisoning by wrapping an interchangeable payload in a banner, a set of benign-looking structural elements that induces an imitating agent to reproduce the enclosed code. The attacker plants malicious skills in the library without invoking them. The agent then authors and executes new skills carrying the harmful code. Each authored copy can re-enter the library and be imitated again, forming a self-propagating worm that persists after the planted skills are removed. We define the agent self-poisoning rate (ASPR) as the fraction of tasks that add a newly authored malicious skill to the library. Across six models on 153 tool-relevant SWE-bench Verified tasks, ASPR ranges from 20.3% to 41.8%, and the poisoned libraries hold 4.9 to 9.0 times as many malicious skills as were planted. The vulnerability also appears without a banner: DeepSeek-V4-Pro reaches 11.1% ASPR with the payload alone. Tailoring the planted skill descriptions to one task family raises ASPR to 86.7%. After the planted skills are removed, Qwen3 retains a round-5 ASPR of 68% because agent-authored copies remain. These copies evade existing defenses, which focus on attacker-submitted names, code, and signatures. We propose counter-prompt, a defense that discourages banner-style copying and reduces EvoMal's ASPR to at most 6.7% with no significant task-completion loss.
Primary: Queen's University
All Institutions: Queen's University
This paper presents a significant and timely contribution to AI security by identifying and exploiting a novel "self-poisoning" vulnerability in self-evolving coding agents, demonstrating how malicious skills can propagate autonomously through imitation, and providing effective countermeasures.
The paper introduces a novel attack vector termed "self-poisoning" targeting self-evolving LLM coding agents. The methodology relies on the agent's tendency to imitate retrieved skills. By planting a malicious skill with a specific "banner" (structural template) in a shared library, the attacker induces the agent to reproduce the malicious payload when authoring new skills. This creates a self-propagating worm effect where the malicious code persists and multiplies even after the original planted skills are removed. The approach is theoretically sound and exploits a specific architectural vulnerability in tool-use agents that rely on imitation learning for skill acquisition.
The evaluation is rigorous and comprehensive. The authors test across six different LLMs (including recent models like DeepSeek-V4-Pro and Qwen3) on 153 SWE-bench Verified tasks. They introduce a new metric, ASPR (Agent Self-Poisoning Rate), to quantify the vulnerability. Results show significant success rates (up to 41.8% ASPR generally, and 86.7% with tailored descriptions). The study also demonstrates the persistence of the attack (round-5 ASPR of 68%) and the failure of existing defenses. Crucially, they propose and evaluate a counter-prompt defense that significantly reduces the attack success rate without harming task completion performance.
The paper provides detailed descriptions of the attack mechanism, the threat model, and the evaluation setup. The use of standard benchmarks (SWE-bench Verified) and multiple open-source/closed-source models enhances reproducibility. The authors mention an open science appendix, suggesting code or detailed implementation details are available, which supports reproducibility.
The attack requires the ability to plant skills in the library, which assumes a level of access or trust in the library curation process that may not always be present. The effectiveness of the "banner" technique might vary depending on the specific model's instruction-following capabilities and the robustness of the retrieval mechanism. The study focuses on coding agents; generalization to other types of agents (e.g., reasoning-only or non-coding) is not fully explored.
This work has significant implications for the security of autonomous AI agents, particularly those operating in shared environments with skill libraries. It highlights a critical supply-chain-like vulnerability in self-evolving systems. The findings will likely influence the design of safer agent architectures, the development of more robust skill verification mechanisms, and the establishment of security standards for shared AI resources. The proposed defense offers a practical mitigation path. This paper presents a significant and timely contribution to AI security by identifying and exploiting a novel "self-poisoning" vulnerability in self-evolving coding agents, demonstrating how malicious skills can propagate autonomously through imitation, and providing effective countermeasures.
Search-augmented reasoning remains difficult for small language models. On-policy distillation (OPD) from trained teachers offers a promising direction, but suffers from two issues: (1) high-quality multi-turn search trajectories depend on dynamic retriever responses, making SFT data prohibitively expensive to collect at scale; (2) task-specifically trained teachers incur substantial training cost, while directly applying OPD with an off-the-shelf teacher without task-specific fine-tuning constrains the student to the teacher's performance ceiling and suffers from severe training instability. We propose OPDSearch+, the first distillation paradigm that requires no teacher fine-tuning for search-augmented reasoning. We investigate the role of a frozen off-the-shelf instruct model as the teacher in on-policy distillation, and reveal a key insight: the teacher reshapes the student's policy distribution so that subsequent RL converges to a superior solution that RL alone cannot reach. In stage one, the student interacts with a live search engine and is distilled via a per-position forward KL objective, transferring reasoning decomposition and evidence integration skills without any task-specific teacher training. In stage two, RL refines the distilled student from a richer behavioral foundation, achieving performance that RL alone cannot reach from scratch. Across seven QA benchmarks, OPDSearch+ with a 3B model consistently outperforms all prior 3B RL baselines, achieving gains of 13.1% on HotpotQA and 8.5% on 2WikiMultihopQA.
Primary: University of Chinese Academy of Sciences
All Institutions: University of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, University of Macau, Wuhan University, Georgia Institute of Technology, Northwestern Polytechnical University, University of Hong Kong
[One sentence main contribution]. OPDSearch+ introduces a novel two-stage on-policy distillation and RL refinement framework that leverages a frozen off-the-shelf teacher to stabilize training and enhance performance in search-augmented reasoning for small language models. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper presents a significant advancement in the field of search-augmented reasoning for small language models. By addressing the critical issues of data collection costs and training instability associated with traditional on-policy distillation, OPDSearch+ offers a practical and effective solution. The theoretical analysis of forward-KL divergence in this context is a valuable contribution, providing insights into gradient variance and entropy collapse. The empirical results demonstrate clear improvements over state-of-the-art baselines, particularly in multi-hop reasoning tasks. The work is well-motivated, rigorously evaluated, and has the potential to influence future research in agent-based learning and model distillation.
The paper proposes OPDSearch+, a two-stage framework for search-augmented reasoning in small language models (SLMs). Stage 1 uses On-Policy Distillation (OPD) with a frozen off-the-shelf teacher (Qwen2.5-14B-Instruct) to distill token-level distributions onto student-generated trajectories using a forward-KL objective. Stage 2 refines this distilled policy using Reinforcement Learning (RL) with Group Relative Policy Optimization (GRPO). The core theoretical contribution is the analysis of forward-KL vs. reverse-KL in the context of on-policy distillation for interactive agents, arguing that forward-KL provides better gradient variance control and prevents entropy collapse. The methodology is sound, addressing the distribution shift problem inherent in offline SFT for dynamic retrieval environments. The use of a frozen generalist teacher to provide implicit supervision for retrieval quality is a clever and cost-effective design choice.
The experimental evaluation is comprehensive, covering seven QA benchmarks (NQ, TriviaQA, PopQA, HotpotQA, 2WikiMultihopQA, MuSiQue, Bamboogle). The results show that OPDSearch+ (3B) outperforms all prior 3B RL baselines, including Search-R1 and GiGPO, with significant gains on multi-hop tasks (e.g., +13.1% on HotpotQA). The ablation studies are thorough, comparing against offline SFT, pure RL, pure OPD, and joint optimization, confirming the superiority of the two-stage approach. The analysis of training dynamics and gradient stability supports the theoretical claims. The comparison with reverse-KL variants demonstrates the practical necessity of the proposed forward-KL objective.
The paper provides detailed hyperparameters, model versions (Qwen2.5-3B/14B), and training configurations (veRL framework, 4 H200 GPUs). The description of the loss functions and masking strategies is clear. The code is not explicitly linked in the text provided, but the method relies on standard components (GRPO, KL divergence) and open-source models, making reproduction feasible for researchers with similar resources. The specific clipping parameters and learning rates are mentioned, aiding reproducibility.
The primary limitation is the reliance on a significantly larger teacher model (14B) for distillation, which incurs inference costs during the training phase, although no fine-tuning of the teacher is required. The method is evaluated only on QA benchmarks; its performance on other reasoning tasks (e.g., mathematical reasoning, code generation) is not explored. The "frozen" teacher assumption might limit the transfer of highly task-specific nuances that a fine-tuned teacher could provide, although the authors argue this is mitigated by the subsequent RL stage. The computational cost of generating on-policy trajectories with a live search engine is non-trivial.
This work contributes to the democratization of advanced reasoning capabilities by enabling small models to perform complex search-augmented tasks efficiently. It reduces the dependency on expensive, task-specific teacher training, lowering the barrier to entry for developing capable search agents. The insights into KL divergence directions for stability in interactive settings are broadly applicable to other agent-based learning scenarios. [One sentence main contribution]. OPDSearch+ introduces a novel two-stage on-policy distillation and RL refinement framework that leverages a frozen off-the-shelf teacher to stabilize training and enhance performance in search-augmented reasoning for small language models. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. The paper presents a significant advancement in the field of search-augmented reasoning for small language models. By addressing the critical issues of data collection costs and training instability associated with traditional on-policy distillation, OPDSearch+ offers a practical and effective solution. The theoretical analysis of forward-KL divergence in this context is a valuable contribution, providing insights into gradient variance and entropy collapse. The empirical results demonstrate clear improvements over state-of-the-art baselines, particularly in multi-hop reasoning tasks. The work is well-motivated, rigorously evaluated, and has the potential to influence future research in agent-based learning and model distillation.
We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO). For OLO over the probability simplex $Δ_d$, we give an algorithm with $O(\log d)$ alternating regret that remains a constant for any time horizon $T$, and a matching lower bound. Our constant regret bound significantly improves previous results with $O(\log ^{2/3}d \cdot T^{1/3})$ regret [Cevher, Cutkosky, Kavis, Piliouras, Skoulakis, Viano, NeurIPS 2023, Hait, Li, Luo, Zhang, COLT 2025]. As a result, we obtain alternating learning dynamics with $O(\log d /T)$ convergence to Nash equilibria in two-player zero-sum games and $O(\log d /T)$ convergence to coarse correlated equilibria in two-player general-sum games. This is the first uncoupled learning dynamics with $O(1/T)$ convergence to CCE in two-player general-sum games, while all prior works suffer additional $\log T$ factors. For general OCO over a $d$-dimensional compact convex set, we give an algorithm with $O(d\log (1+T/d))$ alternating regret, improving the previous best of $\widetilde{O}(d^{2/3}T^{1/3})$. We also prove a matching lower bound of $Ω(d\log (1+T/d))$, showing that the $Ω(\log T)$ factor is unavoidable.
Primary: Shanghai University of Finance and Economics
All Institutions: Shanghai University of Finance and Economics, Yale University
The paper makes a significant theoretical contribution by establishing the minimax-optimal alternating regret for OLO and OCO, providing tight upper and lower bounds that improve upon previous state-of-the-art results and enabling faster convergence rates in uncoupled learning dynamics for games.
The paper proposes a novel online learning algorithm, "Alternation-Aware Hedge" (AA-Hedge), specifically designed to minimize alternating regret in Online Linear Optimization (OLO) and Online Convex Optimization (OCO). The core methodological innovation lies in exploiting the structure of alternating updates in two-player games, where the learner observes the opponent's previous move before making their own. The authors derive a potential function analysis that yields a constant $O(d)$ alternating regret for OLO over the simplex, a significant improvement over previous $O(T^{1/3})$ bounds. For general OCO, they propose a continuous variant ("Continuous AA-Hedge") achieving $O(d(1+T/d))$ regret. The theoretical framework is rigorous, involving detailed proofs of feasibility, potential monotonicity, and matching lower bounds for both settings. The extension from discrete simplex to general convex bodies via measure-theoretic arguments is technically sophisticated.
The paper is primarily theoretical. It does not include empirical experiments on standard game benchmarks or large-scale simulations. The "evaluation" consists of theoretical lower bound constructions and asymptotic complexity analysis. While the theoretical results are strong, the lack of empirical validation on practical game scenarios (e.g., poker variants, large-scale matrix games) limits the immediate assessment of practical performance compared to the theoretical bounds. However, in theoretical ML research, rigorous bounds often suffice for high-impact publication if the problem is well-posed and the bounds are tight.
The algorithms (AA-Hedge and Continuous AA-Hedge) are fully described with pseudocode and parameter settings. The proofs are provided in the appendix. The work is highly reproducible from a theoretical standpoint. The authors acknowledge the use of AI assistants for proof generation, which is a transparency note but does not invalidate the mathematical content provided it is verified (which the text claims was done).
The primary limitation is the lack of empirical validation. The theoretical bounds are asymptotic; finite-sample performance or behavior on non-convex/non-smooth variants of games is not addressed. Additionally, the "alternating" setting assumes a specific turn-based structure which, while common in some game-playing contexts (like CFR variants), is less standard than simultaneous move dynamics in many general ML applications. The $O(d)$ constant might be large in practice for high-dimensional $d$, though it is independent of $T$.
This work settles a long-standing open question regarding the minimax-optimal alternating regret. It provides a theoretical foundation for faster convergence in uncoupled learning dynamics for general-sum games, specifically achieving the first $O(1/T)$ convergence to Coarse Correlated Equilibria (CCE) without logarithmic factors. This has significant implications for the design of efficient, decentralized learning algorithms in multi-agent systems and game theory. The paper makes a significant theoretical contribution by establishing the minimax-optimal alternating regret for OLO and OCO, providing tight upper and lower bounds that improve upon previous state-of-the-art results and enabling faster convergence rates in uncoupled learning dynamics for games.
Tensor-valued prediction is fundamental to geometric deep learning, yet uncertainty quantification (UQ) for such outputs remains an open challenge. While E(3)-equivariant neural networks excel at point estimates, they lack rigorous confidence measures. We focus on symmetric rank-2 tensor prediction, where the target has six Kelvin--Mandel coordinates and full uncertainty is represented by a $6\times6$ covariance matrix. We introduce a framework for E(3)-equivariant UQ, modeling the full predictive distribution where both mean and covariance preserve rotational symmetry. Our approach decomposes the covariance into irreducible representations $\mathrm{Sym}^2(ρ_c) \cong 2\times(l=0) \oplus 2\times(l=2) \oplus 1\times(l=4)$. By mapping from the flat Lie algebra $\mathfrak{sym}(6)$ to the curved SPD manifold via matrix exponentiation, we strictly ensure positive-definite covariances while maintaining exact equivariance. Furthermore, we formulate a Log-Euclidean Equivariant Scoring Objective (LE-ESO)---a robust surrogate loss based on the Multivariate Laplace distribution---providing robustness to heavy-tailed errors and stable optimization. Validation on ModelNet40 inertia tensors and Materials Project dielectric tensors demonstrates that our method achieves competitive performance and provides physically consistent, symmetry-preserving uncertainty estimates with useful risk and OOD sensitivity.
Primary: Fudan University
All Institutions: Fudan University, ByteDance, East China University of Science and Technology
This paper presents a rigorous and mathematically elegant framework for equivariant uncertainty quantification in tensor-valued geometric learning, addressing a critical gap in the field by combining representation theory with Riemannian geometry to ensure physically consistent, positive-definite, and rotationally equivariant covariance estimates.
The paper addresses a significant gap in geometric deep learning: the lack of rigorous uncertainty quantification (UQ) for tensor-valued predictions that respects E(3) equivariance. The core methodological contribution is the construction of an equivariant covariance matrix for symmetric rank-2 tensors. The authors correctly identify that standard Cholesky decomposition breaks equivariance in the Kelvin-Mandel representation, while direct regression fails to guarantee positive-definiteness. Their solution—parameterizing the covariance via the matrix exponential of an equivariant operator in the Lie algebra $\mathfrak{sym}(6)$—is mathematically sound and elegant. The decomposition of the covariance into irreducible representations ($l=0, 2, 4$) provides a structured, physically motivated parameterization. The introduction of the Log-Euclidean Equivariant Scoring Objective (LE-ESO) based on the Multivariate Laplace distribution is a practical innovation for robust training. The approach is theoretically rigorous, leveraging representation theory to hard-constrain the network's output space, which is a superior approach to soft regularization.
The experimental validation is conducted on two datasets: ModelNet40 (inertia tensors) and Materials Project (dielectric tensors). The results demonstrate that the proposed method achieves competitive point prediction accuracy while providing physically consistent uncertainty estimates. The visualization of uncertainty ellipsoids on ModelNet40 effectively illustrates the anisotropic nature of the predicted uncertainty. The comparison against deterministic baselines and diagonal UQ models highlights the necessity of full-covariance modeling. However, the paper lacks comparison against other probabilistic geometric deep learning methods (if any exist for this specific task) or Bayesian ensembles, which would strengthen the claim of superiority. The evaluation of "OOD sensitivity" is mentioned but the specific metrics and results are not fully detailed in the provided text snippet, relying on appendices. The performance on Materials Project (MAE 1.55) is reported, but without context on the baseline MAEs, it is difficult to assess the magnitude of improvement in uncertainty calibration.
The paper provides sufficient detail regarding the architecture (using e3nn), the decomposition of irreps, and the loss function. The use of standard libraries (e3nn) and well-defined mathematical operations (matrix exponential, Clebsch-Gordan coefficients) enhances reproducibility. The authors mention an appendix with detailed derivations and hyperparameter sweeps, which is good practice. The code is not explicitly linked in the text provided, but the methodological clarity is high.
The method is currently restricted to symmetric rank-2 tensors. While the authors mention potential extension to rank-4 tensors (elasticity), the computational complexity of the Clebsch-Gordan coefficients and the size of the resulting Lie algebra operators may pose significant challenges. The reliance on the Log-Euclidean metric, while stable, is an approximation of the affine-invariant metric on SPD manifolds; the impact of this choice on the quality of uncertainty estimates is not deeply analyzed. Furthermore, the method assumes that the uncertainty can be fully captured by a Gaussian-like distribution in the Log-Euclidean space, which may not hold for all complex physical systems with multi-modal uncertainties.
This work has significant implications for scientific machine learning, particularly in materials science and computational physics, where reliable uncertainty estimates are crucial for experimental design and risk assessment. By providing a framework for equivariant UQ, it enables safer deployment of geometric neural networks in high-stakes applications. The rigorous treatment of SPD constraints and equivariance sets a new standard for probabilistic geometric deep learning. This paper presents a rigorous and mathematically elegant framework for equivariant uncertainty quantification in tensor-valued geometric learning, addressing a critical gap in the field by combining representation theory with Riemannian geometry to ensure physically consistent, positive-definite, and rotationally equivariant covariance estimates.
Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested. General-domain CoT-faithfulness probes ignore clinical cost, and medical LLM evaluations treat the chain as a black box. We close this gap with a medical perturbation audit: a 30-operator battery edits both the chain and the question with clinically motivated operators (severity reversal, negation flip, demographic swap, evidence ablation), paired with a chain-update times answer-flip joint analysis that classifies each model by its failure mode. Applied to 14 LLMs on four medical QA benchmarks, three independent tests converge: the Chain-Decoupling Rate (CDR; chain does not register the edit and the answer does not flip) is 72.9% panel-wide on clinically meaningful destructive edits, chain corruption leaves accuracy unchanged, and removing CoT prompting does not reduce accuracy. Two board-certified clinicians re-annotate N=197 perturbed questions; 98.5% leave the gold defensible. The pattern holds across medical and reasoning fine-tuning and scale; on the closed-source tier, where the chain text is unavailable, the answer-side signals are consistent with the same decoupling. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.
Primary: Eindhoven University of Technology
All Institutions: Eindhoven University of Technology, Dana-Farber Cancer Institute
This paper presents a comprehensive and empirically robust audit of medical Chain-of-Thought reasoning, revealing a high rate of decoupling between generated rationales and model decisions, thereby providing a critical safety assessment for the deployment of LLMs in clinical settings.
The paper introduces a rigorous perturbation audit framework specifically designed for medical Chain-of-Thought (CoT) reasoning. The core methodological contribution is the "Chain-Decoupling Rate" (CDR), which jointly analyzes whether the generated reasoning chain registers clinically meaningful edits to the question and whether the final answer flips accordingly. The authors define a 30-operator battery of perturbations, split into chain-level (structural/surface edits to the CoT) and question-level (clinical content edits like severity reversal, negation, demographic swap). This dual-path design allows for a fine-grained taxonomy of failure modes (e.g., faithful/robust vs. decoupled/narrative). The approach is methodologically sound, moving beyond simple accuracy metrics to probe the causal link between reasoning text and decision output.
The evaluation is extensive, covering 14 LLMs (open-weight, reasoning-tuned, and closed-source) across four major medical benchmarks (MedQA, MedMCQA, PubMedQA, MMLU-med). The results are striking and consistent: a panel-wide CDR of 72.9%, indicating that in nearly three-quarters of cases with clinically meaningful destructive edits, the model's chain does not register the change and the answer does not flip. The paper provides robust ablation studies, including clinician validation of the perturbations (98.5% of edits left the gold answer defensible, confirming the edits were clinically valid but non-disruptive to the ground truth), sensitivity analyses, and checks for contamination (option-shuffle tests). The convergence of evidence across open and closed-source models strengthens the validity of the findings.
The paper provides detailed descriptions of the perturbation operators, including regex patterns and seed-driven variants, which aids reproducibility. The authors mention that the reference Python implementation is available from the corresponding author, though a public GitHub link is not explicitly in the text provided. The experimental setup, including model versions, decoding parameters, and prompt templates, is clearly documented. The use of standard benchmarks ensures that other researchers can replicate the baseline comparisons.
The authors acknowledge several limitations. The study is restricted to multiple-choice questions, which may not fully capture the complexity of free-text clinical reasoning. The "chain-update" metric relies on token matching or an LLM-judge, which might miss semantic paraphrasing of the evidence. The closed-source models were evaluated only on answer-side signals due to API restrictions, limiting the direct measurement of CDR for these models. Additionally, the demographic scope is limited to binary gender and age, and the datasets are English-centric.
This paper has significant implications for the deployment of LLMs in healthcare. By demonstrating that CoT rationales are often "decorative" rather than causally load-bearing, it challenges the assumption that clinicians can rely on these rationales for auditability or trust. It highlights a critical safety gap: models can be accurate while being unfaithful, potentially hiding incorrect reasoning. The proposed framework provides a reusable yardstick for auditing faithfulness, which is crucial for developing safer and more reliable medical AI systems. This paper presents a comprehensive and empirically robust audit of medical Chain-of-Thought reasoning, revealing a high rate of decoupling between generated rationales and model decisions, thereby providing a critical safety assessment for the deployment of LLMs in clinical settings.
Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality. Existing methods based on video diffusion model (VDM) commonly rely on incomplete conditioning signals such as sparse point clouds or 2D panoramas, leading to stochastic hallucinations, long-term drifts and suboptimal 3D consistency. We present SpatialCrafter, a novel two-stage framework that addresses these issues by introducing a global 3D proxy for high-fidelity image-to-scene generation. Specifically, we decompose the generation process into global proxy generation and appearance refinement. For proxy generation, we propose a Point-anchored Sparse Structure~(PaSS) Flow module that predicts a spatially aligned and geometrically consistent 3D proxy. For appearance refinement, we re-frame the VDM as a Generative Deferred Refiner which synthesizes high-frequency photorealistic details upon proxy-defined scene geometry. To better integrate the proxy with the pre-trained VDM, we introduce Parallel Geometry Injection and Proxy-Aware Corruption training strategies, which improve robustness to proxy artifacts without disrupting the pretrained generative manifold. Furthermore, as no suitable dataset exists for this explorable scene generation task, we construct a new large-scale dataset of 115K scenes. To the best of our knowledge, it is the first hybrid dataset for image-to-scene generation. Extensive experiments on both synthetic and real-world datasets show that SpatialCrafter outperforms state-of-the-art methods, mitigates long-term drift, and remains robust and consistent under rapid camera motion and extreme viewpoint changes. Code, models, and the newly constructed dataset will be publicly released. See more at https://fangchuan.github.io/SpatialCrafter/.
Primary: Hong Kong University of Science and Technology
All Institutions: Hong Kong University of Science and Technology, Alibaba Group (Tongyi Lab), Jilin University, ManyCore Tech Inc
SpatialCrafter introduces a two-stage framework using a global 3D proxy to enable high-fidelity, 3D-consistent image-to-scene generation, effectively mitigating long-term drift and hallucinations common in video diffusion models. By decomposing generation into proxy creation and appearance refinement, and contributing a large-scale dataset, the paper makes a substantial contribution to the field of world modeling and 3D generation, offering a robust solution for creating explorable environments from single images.
The paper proposes SpatialCrafter, a two-stage framework for single-image world modeling. The core innovation is the introduction of a "global 3D proxy" to anchor the generation process, decomposing it into proxy generation (using a Point-anchored Sparse Structure Flow module) and appearance refinement (using a Generative Deferred Refiner based on Video Diffusion Models). The method addresses the common issues of stochastic hallucinations and long-term drift in existing video diffusion approaches by enforcing geometric consistency through the proxy. The introduction of Parallel Geometry Injection and Proxy-Aware Corruption training strategies is a technically sound approach to integrating 3D constraints without disrupting the pre-trained generative manifold. The methodology is coherent and directly targets a significant bottleneck in current image-to-scene generation.
The authors construct a new large-scale dataset of 115K scenes, which is a significant contribution given the lack of suitable data for this specific task. Experiments on both synthetic and real-world data demonstrate that SpatialCrafter outperforms state-of-the-art methods, particularly in mitigating long-term drift and maintaining consistency under rapid camera motion. The qualitative results described in the abstract suggest high-fidelity photorealistic details. However, without access to the full quantitative tables in the provided text, the depth of the experimental comparison is inferred from the abstract's claims of superiority over SOTA.
The paper promises to release code, models, and the newly constructed dataset publicly. The detailed description of the two-stage framework and specific modules (PaSS Flow, Generative Deferred Refiner) provides a clear roadmap for reproduction. The construction of a hybrid dataset further enhances reproducibility by providing a standardized benchmark for future work in this niche area.
The reliance on a pre-trained Video Diffusion Model (VDM) may limit the method's applicability to domains where such models are not well-trained or where the computational cost of VDMs is prohibitive. The "proxy" approach, while effective for consistency, may introduce artifacts if the initial proxy generation is inaccurate, although the paper claims robustness to proxy artifacts. The method is specifically tailored for explorable scenes, which may not generalize to all types of 3D content generation.
This work has significant implications for gaming, robotics, and VR by enabling the creation of explorable, 3D-consistent worlds from a single image. The release of a large-scale dataset will likely accelerate research in image-to-scene generation. The approach of using 3D proxies to guide diffusion models could inspire similar hybrid approaches in other generative tasks requiring structural consistency. SpatialCrafter introduces a two-stage framework using a global 3D proxy to enable high-fidelity, 3D-consistent image-to-scene generation, effectively mitigating long-term drift and hallucinations common in video diffusion models. By decomposing generation into proxy creation and appearance refinement, and contributing a large-scale dataset, the paper makes a substantial contribution to the field of world modeling and 3D generation, offering a robust solution for creating explorable environments from single images.
Joint text-to-video-audio generation produces synchronized visual and acoustic content, but the long sampling trajectories and heterogeneous multimodal computation of large models make inference prohibitively expensive. We present TurboT2VA, a distillation and inference framework for accelerating a 19B-parameter joint video-audio model. Large-scale T2VA distillation is challenged by modality-imbalanced optimization, the difficulty of continuous-time consistency training at scale, and the quality--diversity trade-off. TurboT2VA addresses these issues with per-modality normalization and a progressive curriculum comprising discrete consistency warm-up, continuous consistency refinement, and joint consistency--distribution matching. The curriculum first establishes a stable, diverse generation trajectory and only then introduces distribution-level refinement. On LTX-2, four-step distillation reduces generator latency from 50.52s to 2.51s at the standard evaluation resolution of 512$\times$768, achieving a 20.1$\times$ speedup while maintaining strong visual quality, audio fidelity, diversity, and video-audio synchronization. We further develop an architecture-aware inference stack that combines guarded W8A8 and fused operators, padded-text compaction, and modality-aware sparse attention while preserving dense cross-modal and text-conditioning paths. Under the high-resolution deployment setting at 1024$\times$1792, the complete stack reduces generator latency from 318.74s to 5.83s on one NVIDIA H20, achieving a 54.67$\times$ generator-only speedup. Inference code and generation demos are available at https://github.com/thu-ml/TurboDiffusion/tree/main/turbot2va.
Primary: Tsinghua University
All Institutions: Tsinghua University, Zhejiang University, National University of Singapore, Qingdao University, Tianjin University, Lionrock AI
[One sentence main contribution]. TurboT2VA introduces a progressive curriculum for score-regularized consistency distillation and an architecture-aware inference stack to accelerate 19B-parameter joint text-to-video-audio generation, achieving up to 54x speedup with minimal quality loss. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. This paper makes a substantial contribution to the field of efficient multimodal generation. By successfully extending consistency distillation and distribution matching to a 19B-parameter joint video-audio model, it addresses critical challenges in modality imbalance and training stability. The proposed three-stage curriculum effectively balances the quality-diversity trade-off, a known issue in few-step generation. Furthermore, the integration of a specialized inference stack demonstrates a holistic approach to deployment efficiency, combining algorithmic improvements (distillation) with systems optimizations (quantization, sparse attention). The rigorous evaluation against state-of-the-art closed and open-source models validates the effectiveness of the approach. While the core distillation techniques are not entirely new, their application and adaptation to the complex, heterogeneous domain of joint video-audio generation, along with the detailed systems engineering, represent a significant step forward for practical deployment of large multimodal models. The work is highly relevant to researchers and practitioners working on efficient diffusion/flow models and multimodal synthesis.
The paper proposes TurboT2VA, a framework for accelerating 19B-parameter joint text-to-video-audio (T2VA) generation. The core methodological contribution is a progressive curriculum for score-regularized consistency distillation (sCM) combined with distribution matching distillation (DMD). The authors address three key challenges in joint modality distillation: modality imbalance (via per-modality normalization), numerical instability (via a three-stage curriculum: dCM warm-up, sCM refinement, sCM+DMD joint), and the quality-diversity trade-off. Additionally, they develop an architecture-aware inference stack involving modality-aware sparse attention (SageSLA), W8A8 quantization, and fused operators. The approach is technically sound, leveraging recent advances in consistency distillation and efficient inference. However, the novelty is somewhat incremental; it applies known distillation and acceleration techniques (sCM, DMD, W8A8, sparse attention) to a new, complex multimodal domain (T2VA) rather than introducing fundamentally new theoretical mechanisms. The "joint" aspect is significant but builds heavily on existing single-modality precedents.
The experimental evaluation is comprehensive and rigorous. The authors evaluate on LTX-2, comparing against closed-source (Sora 2, Kling v3, Veo 3) and open-source (JavisDiT, OVI, DaVinci-MagiHuman) baselines. They provide extensive metrics including JavisBench, VBench, TTA-Bench, and specific video-audio synchronization metrics (CAVP, AVH, Desync). The results demonstrate a significant speedup (20.1x at 512x768, 54.67x at 1024x1792) while maintaining competitive or superior quality and synchronization scores compared to the 40-step teacher and other baselines. The ablation studies effectively isolate the contributions of the curriculum stages and the inference components. The inclusion of high-resolution latency breakdowns adds practical value.
The paper provides detailed hyperparameters, training configurations (8 H20 GPUs, 7K steps), and links to code. The description of the inference stack is sufficiently detailed for implementation. The use of standard benchmarks (JavisBench, VBench) aids in reproducibility and comparison.
The primary limitation is the reliance on a specific teacher model (LTX-2) and the computational cost of the distillation process itself (though reduced by the curriculum). The inference stack, while effective, introduces complexity and potential compatibility issues (e.g., fallback to BF16 for unsupported shapes). The paper does not extensively discuss the failure modes of the sparse attention or the robustness of the audio synchronization under extreme compression. The "joint" nature means that if one modality fails, the other might be affected, though the paper claims robustness.
This work significantly lowers the barrier to deploying high-quality, synchronized video-audio generation models, which has implications for creative industries, accessibility, and immersive media. By enabling real-time or near-real-time generation on single GPUs, it democratizes access to powerful multimodal models. However, it also raises concerns about the potential misuse of deepfake technology, as the generation quality and synchronization are high. [One sentence main contribution]. TurboT2VA introduces a progressive curriculum for score-regularized consistency distillation and an architecture-aware inference stack to accelerate 19B-parameter joint text-to-video-audio generation, achieving up to 54x speedup with minimal quality loss. [Comprehensive analysis of the technical contribution, methodology, and significance to the field]. This paper makes a substantial contribution to the field of efficient multimodal generation. By successfully extending consistency distillation and distribution matching to a 19B-parameter joint video-audio model, it addresses critical challenges in modality imbalance and training stability. The proposed three-stage curriculum effectively balances the quality-diversity trade-off, a known issue in few-step generation. Furthermore, the integration of a specialized inference stack demonstrates a holistic approach to deployment efficiency, combining algorithmic improvements (distillation) with systems optimizations (quantization, sparse attention). The rigorous evaluation against state-of-the-art closed and open-source models validates the effectiveness of the approach. While the core distillation techniques are not entirely new, their application and adaptation to the complex, heterogeneous domain of joint video-audio generation, along with the detailed systems engineering, represent a significant step forward for practical deployment of large multimodal models. The work is highly relevant to researchers and practitioners working on efficient diffusion/flow models and multimodal synthesis.
Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models keep the entire reasoning trace in memory via full attention, hard tasks that need long thinking can be prohibitively expensive. However, we find most intermediate reasoning tokens lose importance as the model continues reasoning. This calls into question whether retaining them is worth the cost. Based on this insight, we propose Prefix Sliding, which discards tokens during reasoning that are not part of the prefix or the window of the last few thousand tokens. The prefix has key instructions and tools available to the model, while the most recent tokens are the current reasoning the model is working on. This caps the total memory requirement regardless of how long the model reasons, allowing for efficient long-horizon test-time scaling. Without training, Prefix Sliding can make existing models 3x faster while maintaining performance. Training with Prefix Sliding using reinforcement learning can achieve better performance by enabling scaling to reasoning traces beyond a hundred thousand tokens. Ablations show Prefix Sliding outperforms summarizing intermediate tokens or vanilla sliding window. Our code is at https://github.com/Muennighoff/prefix-sliding
Primary: Stanford University
All Institutions: Stanford University, NVIDIA, IITP (Korea), DSO National Laboratories
Prefix Sliding enables efficient, long-horizon test-time scaling and training for language models by discarding intermediate reasoning tokens, achieving 3x speedups and enabling RL on traces exceeding 100k tokens. This is a highly impactful contribution to the field of efficient LLM inference and training, addressing a critical bottleneck in scaling reasoning capabilities.
The paper proposes "Prefix Sliding," a technique to enable efficient test-time scaling by discarding intermediate reasoning tokens while retaining the prompt prefix and a sliding window of recent tokens. The core insight is that intermediate reasoning steps often lose importance once their results are computed, allowing for constant-cost generation regardless of trace length. The authors implement custom FlashAttention kernels to support this pattern efficiently and demonstrate its use in both inference-only settings and reinforcement learning training (via truncated backpropagation). The methodology is sound, leveraging existing efficient attention mechanisms (sliding window) but applying them specifically to the structure of Chain-of-Thought reasoning. The integration with RL is a significant technical contribution, addressing the memory bottleneck that previously limited long-horizon reasoning training.
The evaluation covers standard reasoning benchmarks (GPQA, MATH500, AIME25) and demonstrates that Prefix Sliding matches full-attention performance while being 3x faster at long context lengths. The paper includes ablations against "Last k" and "Summary" methods, showing superior efficiency and performance. The training experiments show that enabling RL on 100k+ token traces yields better performance than truncated traces. The results are consistent and well-supported by figures. However, the evaluation is primarily on math and logic tasks; performance on code or open-ended generation is less emphasized, though LiveCodeBench is mentioned. The comparison with other KV-cache eviction methods like H2O is noted but not deeply integrated due to infrastructure differences.
The authors provide a GitHub repository with code and detailed descriptions of the kernel implementation and training setup. The use of standard libraries (vLLM, TRL, Prime-RL) and clear hyperparameter reporting enhances reproducibility. The custom kernels are described sufficiently for implementation.
The paper acknowledges limitations regarding "information loss" on tasks requiring long-range dependency in code (LiveCodeBench), where the sliding window might evict critical context. It also notes limited benefit for short generations due to the "warm-up" phase of the sliding window. The method assumes that intermediate tokens are less important, which may not hold for all reasoning styles or complex multi-step dependencies that require revisiting earlier steps.
This work significantly lowers the barrier for long-horizon reasoning in LLMs by making it computationally feasible. It enables models to "think" longer without prohibitive costs, potentially unlocking new capabilities in complex problem solving. It also provides a practical path for training models on very long reasoning traces, which is crucial for the next generation of reasoning models. Prefix Sliding enables efficient, long-horizon test-time scaling and training for language models by discarding intermediate reasoning tokens, achieving 3x speedups and enabling RL on traces exceeding 100k tokens. This is a highly impactful contribution to the field of efficient LLM inference and training, addressing a critical bottleneck in scaling reasoning capabilities.
General audio comprehension now covers speech, sound, and music over durations from seconds to hours, driven by large audio-language models (LALMs) that are increasingly omni-modal. Yet the benchmarks that test them still rely on clips of seconds, where scores saturate and models converge; recent long-form efforts extend duration but evaluate long audio much as short clips are. We introduce AudioSpan, a benchmark that spans both duration and depth: it pairs audio from 10 minutes to over 2 hours with 3,240 questions across three cognitive levels, namely perception, understanding, and reasoning. Two paths supply the questions, differing in how question content is sourced and how ground truth is obtained. Native QA extracts questions from the audio's content, posing each as a multiple-choice item and an open-ended one graded by detailed rubrics. Anchor QA instead injects ground truth, planting acoustic anchors into the audio and building a perception-to-reasoning chain scored only to the first error. A fully automated pipeline constructs every item through structured captioning, QA generation, and adversarial critic feedback. Evaluating 12 LALMs on AudioSpan, we find the hard part comes before reasoning: distilling a few relevant facts from a long, redundant signal. This difficulty grows with audio length and falls hardest on perception, especially temporal grounding. AudioSpan is available at https://huggingface.co/datasets/holvan/AudioSpan.
Primary: Tsinghua University
All Institutions: Tsinghua University, Alibaba Group, The Chinese University of Hong Kong
The paper introduces AudioSpan, a rigorous benchmark for long-form audio comprehension that effectively bridges the gap between short-clip saturation and the need for deep reasoning evaluation. By combining native content extraction with synthetic anchor injection, it provides verifiable ground truth for complex perception-to-reasoning chains, revealing that current models struggle primarily with temporal grounding and information distillation from long, redundant audio streams rather than high-level reasoning.
The paper proposes AudioSpan, a benchmark designed to evaluate Large Audio-Language Models (LALMs) on long-form audio (10 minutes to 2+ hours) across three cognitive levels: perception, understanding, and reasoning. The methodology is robust, featuring a dual-path question generation strategy: "Native QA" which extracts questions from structured captions of in-the-wild audio, and "Anchor QA" which injects specific acoustic anchors (addition, deletion, modification) to create verifiable ground truth for perception-to-reasoning chains. The automated pipeline includes structured captioning, adversarial critic feedback to remove shortcut-able questions, and multi-level solver verification. This approach effectively addresses the saturation of short-clip benchmarks and the lack of verifiable ground truth in long-form audio.
The authors evaluate 12 recent LALMs (7 open-source, 5 proprietary) and 3 text-only baselines. Key findings include: (1) Current models are bottlenecked by the difficulty of distilling relevant facts from long, redundant audio signals before reasoning can occur. (2) There is a significant gap between closed and open-source models, particularly on long-form inputs where open models are often truncated. (3) Performance declines steadily with audio duration. (4) Perception, specifically temporal grounding and localization, is the weakest cognitive level, and multiple-choice success is a poor proxy for open-ended grounded comprehension (low AUC between MCQ correctness and Rubric score). The experiments are comprehensive, covering duration tiers, cognitive levels, and question formats.
The paper provides high reproducibility. The dataset is released on HuggingFace. The construction pipeline, including captioning, QA generation, and quality assurance gates, is detailed in the appendix. Evaluation protocols, scoring rubrics, judge prompts, and model specifications are provided. The use of specific models for each pipeline stage (ASR, captioning, generation, judging) is listed, allowing for potential replication of the benchmark construction process.
The benchmark relies on an automated pipeline for question generation, which, despite adversarial filtering, may still contain subtle biases or errors inherent to LLM-generated content. The "Anchor QA" path, while providing verifiable ground truth, involves synthetic modifications to audio which may not fully reflect naturalistic long-form audio challenges. The evaluation of open-source models is limited by their context window constraints (truncation), which may not reflect their true capability if longer contexts were available. The use of LLM judges for open-ended questions introduces potential subjectivity, although the rubric-based approach mitigates this.
This benchmark is highly significant for the audio AI community as it shifts the focus from short-clip recognition to long-form comprehension and reasoning. It provides a necessary tool for evaluating the "omni-modal" capabilities of modern LALMs. The finding that temporal grounding is a critical failure point will likely drive future research into better temporal attention mechanisms and audio-encoder architectures. The dual-path design (Native vs. Anchor) offers a flexible framework for creating future benchmarks in other modalities. The paper introduces AudioSpan, a rigorous benchmark for long-form audio comprehension that effectively bridges the gap between short-clip saturation and the need for deep reasoning evaluation. By combining native content extraction with synthetic anchor injection, it provides verifiable ground truth for complex perception-to-reasoning chains, revealing that current models struggle primarily with temporal grounding and information distillation from long, redundant audio streams rather than high-level reasoning.
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simply by specifying it in the context, without any parameter update. This form of in-context learning (ICL) turns generalization into a problem of task specification. To achieve cross-task generalization, we bring this paradigm to robotic manipulation, and argue that the natural task specification for manipulation is a human video: unlike language, it provides rich visual cues about the intended task evolution. We present Zero-WAM, a causal video-action model that executes unseen tasks by following in-context human video guidance. To address the scarcity of task-rich paired human-robot data, we propose an automatic pipeline that converts task-sampled robot trajectories into semantically matched human videos, yielding HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks. For model training, we further introduce an in-context future chunk prediction (IFP) objective that suppresses shortcuts learned from seen tasks and forces the policy to draw task information from the video prompt. On seven unseen tasks in RoboTwin 2.0 simulation, Zero-WAM achieves a 47.0% average success rate, an absolute improvement of 29.5 percentage points over the strongest video-action baseline. In real-world evaluations, it follows human video guidance to generalize to unseen task configurations involving multi-object scenes, long-horizon manipulation, and fine-grained insertion.
Primary: Ant Group (Robby Ant Research)
All Institutions: Ant Group (Robby Ant Research)
[One sentence main contribution]. [Zero-WAM introduces a novel in-context learning framework for robotic manipulation that leverages automatically generated human video demonstrations and a specialized future chunk prediction objective to achieve significant zero-shot cross-task generalization performance.]
The paper proposes Zero-WAM, a causal video-action model designed for zero-shot cross-task generalization in robotic manipulation. The core innovation lies in treating human video demonstrations as in-context learning (ICL) prompts, analogous to how LLMs use text instructions. To overcome the scarcity of paired human-robot data, the authors introduce an automatic pipeline (HumanGen) that synthesizes human videos from robot trajectories using VLMs and video generation models. A key technical contribution is the In-Context Future Chunk Prediction (IFP) objective, which prevents the model from relying on shortcuts from robot history by forcing it to predict strided future video chunks based on the current state, thereby encouraging the encoding of task semantics from the human video prompt. The architecture utilizes a Mixture-of-Transformers design, separating video and action prediction while sharing attention layers.
The evaluation is conducted on the RoboTwin 2.0 simulation environment and real-world Franka robot setups. The paper reports a significant improvement over strong baselines (LingBot-VA and WAN-Action), achieving a 47.0% average success rate on seven unseen tasks, an absolute improvement of 29.5 percentage points. The results are supported by ablation studies isolating the effects of ICL, IFP, and task-balanced data sampling. Real-world experiments further demonstrate generalization to unseen configurations in multi-object scenes and fine-grained insertion tasks. The statistical significance is addressed by reporting results over multiple seeds and trials.
The paper provides detailed implementation details, including model architecture (Wan-2.2 backbone), training hyperparameters, and data composition. The dataset (HumanGen) and code are likely to be released given the project URL, though the current text does not explicitly confirm code availability. The methodology for generating synthetic human videos is described in sufficient detail for replication.
The reliance on synthetic human videos introduces potential domain gaps or "hallucinations" in the generated demonstrations, although the paper claims semantic preservation. The current evaluation is limited to stationary tabletop manipulation; generalization to mobile manipulation or highly dynamic environments is not demonstrated. The success on the "stack blocks three" task remains low (9%), indicating challenges with long-horizon, complex spatial reasoning.
This work advances the field of robot learning by providing a scalable path to zero-shot generalization using abundant human video data. It reduces the dependency on expensive, task-specific robot demonstrations. The potential for widespread adoption of human-video-guided policies could accelerate the deployment of general-purpose robots in unstructured environments. [One sentence main contribution]. [Zero-WAM introduces a novel in-context learning framework for robotic manipulation that leverages automatically generated human video demonstrations and a specialized future chunk prediction objective to achieve significant zero-shot cross-task generalization performance.]
Steering-based planners require solutions to state-to-state boundary value problems, which can be inaccessible for nonlinear platforms. Forward propagation evades the steering requirement, but the finite-sample behavior of the associated planners remains uncharacterized and their implementations underperform in practice. This paper develops a propagation-based kinodynamic planner with deterministic finite-sample near-optimality guarantees. We work within the large class of differentially flat nonlinear systems and show that a forward tree of locally dispersive control commands contains a near-optimal trajectory at a certified tree size. We provide a general mechanism to construct dispersive command sets for control-affine systems, which are necessary to implement the search algorithm prescribed by the theory. We show that covering the certified trajectory class irrespective of cost provably demands a tree exponentially sized in the problem horizon, and present a cost-conditioned dominance pruning procedure that retains near-optimality at a tree size polynomial in the horizon. We implement the resulting search algorithm, Dispersive Forward Tree search (DFT*), as breadth-first expansion of the forward tree, which maps naturally onto parallel hardware. We design efficient dispersive samplers for the unicycle, the trailer car, and the quadrotor and evaluate challenging planning tasks for these platforms. DFT* delivers consistently competitive and often substantially better solution quality than state-of-the-art kinodynamic planners at comparable solution times on embedded-tier processors, accelerating further as parallel compute is scaled. We also implement DFT* in a receding-horizon loop to demonstrate real-time planning in dynamic environments at embedded-tier compute budgets.
Primary: Massachusetts Institute of Technology
All Institutions: Massachusetts Institute of Technology
The paper provides the first deterministic finite-sample near-optimality guarantees for forward-propagation kinodynamic planners on differentially flat systems, bridging a critical gap in sampling-based planning theory. By introducing dispersive command sets and a polynomial-time pruning procedure, the authors develop DFT*, a practical algorithm that achieves state-of-the-art performance on embedded hardware, offering a rigorous and efficient alternative to steering-based planners for nonlinear robotic systems.
The paper introduces Dispersive Forward Tree search (DFT*), a kinodynamic planner that leverages differential flatness to avoid solving difficult state-to-state boundary value problems (BVPs). The core theoretical contribution is the development of deterministic, finite-sample near-optimality guarantees for forward-propagation-based planners, a gap previously filled only by probabilistic or asymptotic guarantees. The authors define "locally dispersive" command sets and prove that covering the certified trajectory class requires an exponentially sized tree in the horizon. To make this tractable, they introduce a cost-conditioned dominance pruning procedure that reduces the tree size to polynomial in the horizon while retaining near-optimality. The method is theoretically sound, building on metric entropy and dispersion theory, and provides a rigorous bridge between sampling-based planning and optimal control theory for nonlinear systems.
The evaluation is comprehensive, testing DFT* on unicycle, trailer car, and quadrotor models. The authors compare against state-of-the-art kinodynamic planners on the Dynobench suite, demonstrating competitive or superior solution quality at comparable or faster solution times on embedded-tier processors. A key strength is the demonstration of real-time planning in dynamic environments using a receding-horizon loop (WWDFT*), validating the practical utility of the theoretical bounds. The use of parallel hardware (GPU/SIMD) is well-integrated, showing significant speedups.
The authors provide a public GitHub repository with the code, which significantly enhances reproducibility. The paper details the construction of dispersive samplers for specific platforms, allowing others to adapt the method to different differentially flat systems. The theoretical proofs are included in the appendix, providing a complete reference for the guarantees.
The method is restricted to differentially flat systems, which, while a large class, excludes many complex robotic systems (e.g., those with non-holonomic constraints that are not flat). The exponential complexity in the horizon, even with pruning, may still pose challenges for very long-horizon planning tasks. The reliance on differential flatness requires specific system properties that may not hold for all practical robots.
This work has significant implications for the robotics and optimal control communities by providing the first deterministic finite-sample guarantees for forward-propagation planners. It offers a principled alternative to steering-based methods for nonlinear systems, potentially leading to more robust and efficient planning algorithms for autonomous vehicles and aerial robots. The integration of parallel computing techniques also sets a precedent for high-performance planning implementations. The paper provides the first deterministic finite-sample near-optimality guarantees for forward-propagation kinodynamic planners on differentially flat systems, bridging a critical gap in sampling-based planning theory. By introducing dispersive command sets and a polynomial-time pruning procedure, the authors develop DFT*, a practical algorithm that achieves state-of-the-art performance on embedded hardware, offering a rigorous and efficient alternative to steering-based planners for nonlinear robotic systems.